Immunotherapy represents one of the promising approaches for the treatment of cancer. In this approach, a patient's immune system is recruited to fight against tumor development and growth. The most successful immunotherapeutics to date have been immune checkpoint inhibitors, including antibodies that bind to programmed cell death protein 1 (PD-1), PD-L1, or CTLA-4.
The efficacy of immunotherapy has been demonstrated in several studies. However, such treatment is only effective in in a subset of patients. As such, there is currently an intense effort to develop methods for identifying patients that are likely to respond to immunotherapeutic drugs.
Rizvi et al (Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015 348: 124-128) shows that the mutation load of a tumor (i.e., the number of nonsynonymous point mutations associated with the tumor), can be predictive for treatment response. Rizvi performed whole exome sequencing of non-small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1), and showed that an increase in the number of nonsynonymous point mutations associated with a tumor (i.e., an increase in tumor mutation load) correlates with improved objective response, durable clinical benefit (DCB), and progression-free survival (PFS).
Measuring the mutational load of a tumor is challenging because: i. mutations are relatively rare events, even in the genome of a cancer cell and ii. samples of DNA from a patient carrying a tumor typically contain a mixture of DNA from the tumor and DNA that is not from the tumor. The latter is particularly problematic for cell-free DNA, which may contain as little as 1% to 10% of DNA from the tumor. As such, in order to measure the mutational load of a tumor, a significant portion, e.g., at least a few hundred kb or more than a Mb, of the genome should be sequenced at a read depth that is sufficient to identify mutations that may only be present at a relatively low frequency (e.g., 1%-10%) in the sample. Mutational load has been estimated using whole-exome sequencing (Rizvi, supra) and by sequencing panels of hundreds of selected cancer-related genes (see, e.g., Campesato, et al. Oncotarget 2015 6: 34221-34227). These methods require enriching regions of the genome (e.g., exons or cancer-related sequences) using hybridization/bait based technologies, and then sequencing the enriched regions. Such methods are multi-step, costly, inefficient and not readily implementable in a high throughput manner.
PCR strategies are generally not used to measure tumor mutational load because, at best, PCR is only able to amplify a few tens of kb of a genome, even in a multiplex PCR reaction. This length is generally insufficient to provide an estimate of mutation load. This problem is compounded by the fact that many of the most accessible patient samples (e.g., liquid biopsies and the like) contain DNA that is highly fragmented, making it impossible to amplify longer fragments.
Better methods for estimating tumor mutational load are therefore needed.